Visualization of 2D and 3D Object Detection System

Authors

  • T. Sridevi  Associate Professor, Department of CSE, Chaitanya Bharathi Institute of Technology, Hyderabad, India
  • S. Sushanth  Student, B.E, Department of CSE, Chaitanya Bharathi Institute of Technology, Hyderabad, India

Keywords:

3D object detection, centralization, objects

Abstract

Existing 3D representations for RGB-D images capture the local shape and appearance of object categories, but have limited power to represent objects with different visual styles. The detection of small objects is also challenging because the search space is very large in 3D scenes. However, we observe that much of the shape variation within 3D object categories can be explained by the location of a latent support surface, and smaller objects are often supported by larger objects. Based on this simple sensor modality for practical applications, deep learning- based monocular 3D object detection methods that overcome significant research challenges are categorized and summarized. This paper gives a visualization of 2d and 3d object detection system.

References

  1. Almasi, Omid N, Rouhani, Modjtaba, “A new fuzzy membership assignment and model selection approach based on dynamic class centers for fuzzy SVM family using the firefly algorithm”, Turkish Journal of Electrical Engineering & Computer Sciences, 4: 1–19, 2016.
  2. Arunkumar Sangiah, Arunkumar Thangavelu, Venkatesan MeenakshiSundram, “Cognitive Computing for Big Data Systems over IoT”, Gewerbestrasse, 11, p.6330, 2018.
  3. Bavya N, Arunkumar T, Adalarasu K, “A Comprehensive Survey on IoT Technologies in Health Care System”, Research Journal of Pharmacy and Technology, Vol. 11, Issue 7, pp. 3157-3162, 2018.
  4. Chen, X., Zheng, Z., Yu, Q., Lyu, M.R., “Web service recommendation via exploiting location and QoS information”, IEEE Trans. Parallel Distrib. Syst., 25(7), pp. 1913 – 1924, 2014.
  5. F. Ding, A. Song, E. Tong, J. Li, “A smart gateway architecture for improving efficiency of home network applications”, Journal of Sensors, 2016.
  6. C. Tao, X. Ling, S. Guofeng, Y. Hongyong, H. Quanyi, “Architecture for monitoring urban infrastructure and analysis method for a smart-safe city”, In Sixth International Conference on Measuring Technology and Mechatronics Automation, pp. 151–154, 2014.
  7. Umadevi K S, ArunkumarThangavelu, “An optimal medium access slot allocation for wimedia medium access control protocol using firefly algorithm”, International conference on Microelectronic Devices, Circuits and Systems (ICMDCS), pg 1-3, DOI: 10.1109/ICMDCS.2017.8211711, August 2017.
  8. C. Xiaojun, L. Xianpeng, X. Peng, “IoT-based air pollution monitoring and forecasting system”, In 2015 International Conference on Computer and Computational Sciences (ICCCS), pp. 257–260, 2015.
  9. Yang, X. S., “Nature-Inspired Metaheuristic Algorithms”, Luniver Press, pp.242-246, 2008. ISBN 978- 1-905986-10-1.
  10. Yin, Jianwei, Wei Lo, Shuiguang Deng, Ying Li, Zhaohui Wu, and Naixue Xiong, “Colbar: A collaborative location-based regularization framework for QoS prediction”, Information Sciences, 265, 68-84, 2014.

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Published

2019-12-30

Issue

Section

Research Articles

How to Cite

[1]
T. Sridevi, S. Sushanth, " Visualization of 2D and 3D Object Detection System, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 6, Issue 6, pp.382-387, November-December-2019.